Method and apparatus for generating information

ABSTRACT

A method and apparatus for generating information are disclosed. An implementation of the method includes: receiving a target text, the target text including an objective and descriptive information of the objective; performing a dependency syntax parsing on the target text to generate a dependency tree of the target text; matching predetermined syntactic structure tree with the dependency treeto obtain at least one triple, a triple including a subject, a predicate, and an object; and determining, based on words contained in a triple among the at least one triple and a predetermined weight of the syntactic structure tree matched to obtain the triple, a target triple among the at least one triple.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent ApplicationNo.201810567936.0, filed with the China National Intellectual PropertyAdministration (CNIPA) on Jun. 5, 2018, the content of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

An embodiment of the present disclosure relate to the field of computertechnology, specifically to a method and apparatus for generatinginformation.

BACKGROUND

At present, Named Entity Recognition (NER) technology and Entity Linking(EL) technology can typically be employed to mine a text for entities.Here, NER is able to recognize proper nouns, such as persons,enterprises. And EL is able to link words in a text to entities in theknowledge graph, and solves the entity co-reference problem. However,recognition and linking of events is currently impossible.

SUMMARY

An embodiment of the present disclosure provides a method and apparatusfor generating information.

In a first aspect, an embodiment of the present disclosure provides amethod for generating information, including: receiving a target text,the target text including an objective and descriptive information ofthe objective; performing a dependency syntax parsing on the target textto generate a dependency tree of the target text; match predetermined atleast one syntactic structure tree with the dependency tree to obtain atleast one triple, a triple including a subject, a predicate, and anobject; and determining, based on words contained in a triple among theat least one triple and a predetermined weight of the syntacticstructure tree matched to obtain the triple, a target triple among theat least one triple.

In some embodiments, the determining, based on words contained in atriple among the at least one triple and a predetermined weight of thesyntactic structure tree matched to obtain the triple, a target tripleamong the at least one triple, includes: determining a quantifier and anattributive within the target text based on the dependency tree;determining an objective modified by the quantifier and an objectivemodified by the attributive; updating the at least one triple based onthe determined quantifier, attributive and objectives; and determiningthe target triple among updated at least one triple.

In some embodiments, the updating the at least one triple based on thedetermined quantifier, attributive and objectives includes: for thetriple among the at least one triple, determining whether the determinedobjective matches the subject or object of the triple; merging, inresponse to determining that the determined objective matches thesubject of the triple, the quantifier and attributive modifying thedetermined objective with the subject of the triple, and determining amerged text as the subject of the triple; and merging, in response todetermining that the determined objective matches the object of thetriple, the quantifier and attributive modifying the determinedobjective with the object of the triple, and determining a merged textas the object of the triple.

In some embodiments, the determining, based on words contained in atriple among the at least one triple and a predetermined weight of thesyntactic structure tree matched to obtain the triple, a target tripleamong the at least one triple includes: for the triple among the atleast one triple, determining the predetermined weight of the syntacticstructure tree matched to obtain the triple; determining a number ofcharacters of the words in the triple, determining a co-occurrencedegree of the words within the triple, and determining a score of thetriple according to the determined weight, number and co-occurrencedegree; and determining a triple with a highest score among the at leastone triple as the target triple.

In some embodiments, the method further includes: obtaining at least onehistorical target triple; statisticising a number of historical targettriples obtained by matching a given syntactic structure tree in the atleast one historical target triple; and determining a weight of the atleast one syntactic structure tree based on a result of thestatisticising.

In some embodiments, the method further includes: determining at leastone piece of historical event information relevant to the target text ina predetermined historical event information set based on the targettriple; determining a similarity between the target text and the atleast one piece of historical event information; and outputtinghistorical event information having highest similarity to the targettext.

In some embodiments, the historical event information includesparticipant information and trigger word information; and thedetermining at least one piece of historical event information relevantto the target text in a predetermined historical event information setbased on the target triple, includes: determining whether followingconditions are met: a subject or an object of the target triple matchesthe participant information of the historical event information withinthe historical event information set, or a predicate of the targettriple matches the trigger word information of the historical eventinformation within the historical event information set; and determiningthe historical event information meeting at least one of the aboveconditions being relevant to the target text.

In some embodiments, the historical event information includes keywords;and the determining a similarity between the target text and the atleast one piece of historical event information includes: segmenting thetarget text to obtain a first word set; and for the historical eventinformation among the at least one piece of historical eventinformation, concatenating keywords in the historical event information,segmenting a text obtained by concatenating, to obtain a second wordset; and determining a similarity between the target text and thehistorical event information based on the first word set and the secondword set.

In a second aspect, an embodiment of the present disclosure provides anapparatus for generating information, including: a target text receivingunit, which is configured to receive a target text, the target textincluding an objective and descriptive information of the objective; adependency tree generating unit, which is configured to perform adependency syntax parsing on the target text to generate a dependencytree of the target text; a triple determining unit, which is configuredto match predetermined at least one syntactic structure tree with thedependency tree to obtain at least one triple, a triple including asubject, a predicate, and an object; and a target triple determiningunit, which is configured to determine, based on words contained in atriple among the at least one triple and a predetermined weight of thesyntactic structure tree matched to obtain the triple, a target tripleamong the at least one triple.

In some embodiments, the target triple determining unit includes: anattributive determining module, which is configured to determine aquantifier and an attributive within the target text based on thedependency tree; an objective determining module, which is configured todetermine an objective modified by the quantifier and an objectivemodified by the attributive; a triple updating module, which isconfigured to update the at least one triple based on the determinedquantifier, attributive and objectives; and a target triple determiningmodule, which is configured to determine the target triple among updatedat least one triple.

In some embodiments, the triple updating module is further configuredto: for the triple among the at least one triple, determine whether thedetermined objective matches the subject or object of the triple; merge,in response to determining that the determined objective matches thesubject of the triple, the quantifier and attributive modifying thedetermined objective with the subject of the triple, and determine amerged text as the subject of the triple; and merge, in response todetermining that the determined objective matches the object of thetriple, the quantifier and attributive modifying the determinedobjective with the object of the triple, and determine a merged text asthe object of the triple.

In some embodiments, the target triple determining unit is furtherconfigured to: for the triple among the at least one triple, determinethe predetermined weight of the syntactic structure tree matched toobtain the triple; determine a number of characters of the words in thetriple, determine a co-occurrence degree of the words within the triple,and determine a score of the triple according to the determined weight,number and co-occurrence degree; and determine a triple with the highestscore among the at least one triple as the target triple.

In some embodiments, the apparatus further includes a weight settingunit, the weight setting unit includes: a historical target triplemodule, which is configured to obtain at least one historical targettriple; a triple number statisticising module, which is configured tostatisticise a number of historical target triples obtained by matchinga given syntactic structure tree in the at least one historical targettriple; and a weight determining module, which is configured todetermine a weight of the at least one syntactic structure tree based ona result of the statisticising.

In some embodiments, the apparatus further includes: a historical eventinformation determining unit, which is configured to determine at leastone piece of historical event information relevant to the target text ina predetermined historical event information set based on the targettriple; a similarity determining unit, which is configured to determinea similarity between the target text and the at least one piece ofhistorical event information; and a historical event informationoutputting unit, which is configured to output historical eventinformation having the highest similarity to the target text.

In some embodiments, the historical event information includesparticipant information and trigger word information; and the historicalevent information determining unit is further configured to: determinewhether following conditions are met: a subject or an object of thetarget triple matches the participant information of the historicalevent information within the historical event information set, or apredicate of the target triple matches the trigger word information ofthe historical event information within the historical event informationset; and determine the historical event information meeting at least oneof the above conditions being relevant to the target text.

In some embodiments, the historical event information includes keywords;and the similarity determining unit is further configured to: segmentthe target text to obtain a first word set; and for the historical eventinformation among the at least one piece of historical eventinformation, concatenate keywords in the historical event information,segment a text obtained by concatenating, to obtain a second word set;and determine a similarity between the target text and the historicalevent information based on the first word set and the second word set.

In a third aspect, an embodiment of the present disclosure provides anapparatus, including: one or more processors; and a storage device, onwhich are stored one or more programs, when the one or more programs areexecuted by the one or more processors, they cause the one or moreprocessors to implement the method as described in any embodiment of thefirst aspect.

In a forth aspect, an embodiment of the present disclosure provides acomputer-readable medium, on which is stored a computer program,wherein, when the program is executed by a processor, it implement themethod as described in any embodiment of the first aspect.

The method and apparatus for generating information provided in theembodiment of the present disclosure, after receiving the target text,may perform a dependency syntax parsing on target text to generate adependency tree of the target text; then, it may match predetermined atleast one syntactic structure tree with the dependency tree to obtain atleast one triple; and finally, a target triple among the at least onetriple is determined based on the words contained in each triple amongthe at least one triple, and a predetermined weight of the syntacticstructure tree matched to obtain the triple. The method and apparatus inthe present embodiments, can pick out a triple that is most relevant tothe event contained in the target text, thereby improving the accuracyof extracting a target triple.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objectives and advantages of the present disclosure willbecome more apparent upon reading the detailed description tonon-limiting embodiments with reference to the accompanying drawings:

FIG. 1 is an exemplary system architecture diagram to which anembodiment of the present disclosure may be applied;

FIG. 2 is a flowchart of an embodiment of a method for generatinginformation according to the present disclosure;

FIG. 2A is a schematic structural diagram of a dependency tree of anembodiment of a method for generating information according to thepresent disclosure;

FIG. 2B is a schematic structural diagram of a syntactic structure treeof an embodiment of a method for generating information according to thepresent disclosure;

FIG. 2C is a schematic structural diagram of a candidate triple,obtained by matching the dependency tree shown in FIG. 2b with thesyntactic structure tree shown in FIG. 2 b, in a method for generatinginformation according to the present disclosure;

FIG. 2D is a schematic structural diagram of another candidate triple,obtained by matching the dependency tree shown in FIG. 2b with thesyntactic structure tree shown in FIG. 2 b, in a method for generatinginformation according to the present disclosure;

FIG. 2E is a schematic structural diagram of yet another candidatetriple, obtained by matching the dependency tree shown in FIG. 2b withthe syntactic structure tree shown in FIG. 2 b, in a method forgenerating information according to the present disclosure;

FIG. 3 is a schematic diagram of an application scenario of a method forgenerating information according to the present disclosure;

FIG. 4 is a flowchart of determining a target triple in a method forgenerating information according to the present disclosure;

FIG. 5 is a flowchart of another embodiment of a method for generatinginformation according to the present disclosure;

FIG. 6 is a schematic structural diagram of an embodiment of anapparatus for generating information according to the presentdisclosure;

FIG. 7 is a schematic structural diagram of a computer system, that isappropriate for implementing an equipment, in an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present disclosure will be further described below in detail incombination with the accompanying drawings and the embodiments. Itshould be appreciated that the specific embodiments described herein aremerely used for explaining the relevant disclosure, rather than limitingthe disclosure. In addition, it should be noted that, for the ease ofdescription, only the parts related to the relevant disclosure are shownin the accompanying drawings.

It should also be noted that the embodiments in the present disclosureand the features in the embodiments may be combined with each other on anon-conflict basis. The present disclosure will be described below indetail with reference to the accompanying drawings and in combinationwith the embodiments.

FIG. 1 illustrates an exemplary system architecture 100 to which anembodiment of a method for generating information or an apparatus forgenerating information of the present disclosure may be applied.

As shown in FIG. 1, the system architecture 100 may include terminaldevices 101, 102, 103, a network 104, and a server 105. The network 104serves as a medium providing a communication link between the terminaldevices 101, 102, 103 and the server 105. The network 104 may includevarious types of connections, such as wired or wireless communicationlinks, or fiber-optic cables.

The user may use the terminal devices 101, 102 and 103 to interact withthe server 105 through the network 104, in order to transmit or receivemessages, etc. various communication client applications, such as textinput applications, web browser applications, shopping applications,search applications, instant messaging tools, mailbox clients, andsocial platform software may be installed on the terminal devices 101,102 and 103.

The terminal devices 101, 102 and 103 may be hardware or software. Whenthe terminal devices 101, 102 and 103 are hardware, they may be variouselectronic devices having display screens and supporting textual input,including but not limited to smart phones, tablets, e-book readers, andMP3 players (Moving Picture Experts Group Audio Layer III), MP4 (MovingPicture Experts Group Audio Layer IV) players, laptop portablecomputers, desktop computers, etc. When the terminal devices 101, 102and 103 are software, they may be installed in the above-listedelectronic devices. They may be implemented as a plurality of softwareor software modules (for example, for providing distributed services),or as a single software or software module, which is not specificallylimited here.

The server 105 may be a server providing various services, for example,a background server supporting the text input on the terminal devices101, 102, and 103. The background server may perform a processing suchas an analysis on data, such as a received target text, and return aprocessing result (for example, a target triple) to the terminal devices101, 102, and 103.

The server 105 may be hardware or software. When the server 105 ishardware, it may be implemented as a distributed server cluster composedof multiple servers, or may be implemented as a single server. When theserver 105 is software, it may be implemented as a plurality of softwareor software modules (for example, for providing distributed services),or as a single software or software module, which is not specificallylimited here.

It should be noted that the method for generating information providedin an embodiment of the present disclosure may be executed by theterminal device 101, 102 or 103, or may also be executed by the server105. Accordingly, the apparatus for generating information may beinstalled on the terminal device 101, 102 or 103, or may also beinstalled on the server 105.

It is appreciated that, if the method for generating information,provided in the embodiments of the present disclosure, is executed bythe terminal device 101, 102 or 103, the system architecture 100 may notinclude the network 104 and the server 105.

It should be appreciated that the numbers of the terminal devices, thenetworks and the servers in FIG. 1 are merely illustrative. Any numberof terminal devices, networks and servers may be provided based on theactual requirements.

With continued reference to FIG. 2, a flow 200 of an embodiment of themethod for generating information according to the present disclosure isillustrated. The method for generating information in the presentembodiment includes following:

Step 201, receiving a target text.

In the present embodiment, an executor of the method for generatinginformation (for example, terminal devices 101, 102, 103, or server 105as shown in FIG. 1) may receive a target text. As a terminal device, theexecutor of the method for generating information may directly receive atarget text that is input by a user through the terminal device. As aserver, the executor of the method for generating information mayreceive a target text, through a wired or wireless connection, from aterminal device operated by a user. The target text may include anobjective, and descriptive information of the objective. The aboveobjective may be any entity that is recognized through NER technology orEL technology, such as a person, or an enterprise. The descriptiveinformation may be information that is used to describe the objective,which includes, but is not limited to, information being used todescribe the status of the objective, and information being used todescribe the action of the objective, etc.

It should be pointed out that the wireless connection may include, butis not limited to, 3G/4G connection, WiFi connection, Bluetoothconnection, WiMAX connection, Zigbee connection, UWB (ultra wideband)connection, and other wireless connections that are known at present orare to be developed in the future.

Step 202, performing a dependency syntax parsing on the target text togenerate a dependency tree of the target text.

After receiving the target text, the executor may perform a dependencysyntax parsing on the target text. Dependency syntax, also calleddependency relation syntax, was first proposed by L. Tesniere, a Frenchlinguist, in 1950s. Dependency syntax is a structural syntax, describinglinguistic structure of a sentence using dependency relations which areformed among words. In order to clearly describe the characteristics ofthe structure in the dependency syntax, the structure may be expressedthrough a dependency tree. Each node in the dependency tree correspondsto a word in the sentence. The dependency tree may characterize not onlythe dependency relationship between the words but also categories of thewords (for example, a quantifier, or a particle), as well as functionsof the words in the text (for example, an attributive, or an adverbial).In practical applications, the executor may perform a dependency syntaxparsing on the target text through various open source toolkits. Theopen source toolkits may include, for example, StandfordParser—an opensource toolkit provided by Stanford NLP Group at Stanford University,Fudan NLP—an open source toolkit developed by School of Computer Scienceat Fudan University in China.

Step 203, matching predetermined at least one syntactic structure treewith the dependency tree to obtain at least one triple.

After generating a dependency tree of the target text, the executor maymatch a predetermined syntactic structure tree with the dependency tree.Here, the tree-like structure of the syntactic structure tree mayinclude a plurality of nodes, and the syntactic structure tree mayinclude a category of a word located in each node. By matching thesyntactic structure tree with the dependency tree, words in thedependency tree having the same dependency relationship with thesyntactic structure tree maybe obtained. Here, the category of eachobtained word is identical to the category of the word of thecorresponding node in the syntactic structure tree.

For example, the structure of a dependency tree of the target text isshown in FIG. 2 a, and the structure of a syntactic structure tree isshown in FIG. 2 b. The syntactic structure tree shown in FIG. 2billustrates the category of a word of each node, namely, v. represents averb, n represents a noun. In some optional embodiments, the syntacticstructure tree may be matched with the dependency tree using followingapproach: first, when only the structures, and not the word categories,of the syntactic structure tree and the dependency tree are considered,candidate triples formed by words located on the dotted-line nodes inFIG. 2 c, FIG. 2 d, and FIG. 2e can be determined. Then, the categoriesof the words at respective nodes in the candidate triples shown in FIG.2 c, FIG. 2d and FIG. 2e are matched with the categories of the words atrespective nodes in the syntactic structure tree, to determine that thecategories of the respective words in the triple shown in FIG. 2c isidentical to the categories of the words at respective nodes in thesyntactic structure tree. Accordingly, the triple shown in FIG. 2c isthe result obtained by matching the syntactic structure tree with thedependency tree.

The triple may include a subject, a predicate, and an object, whereinthe triple may be a triple in a broad sense. For example, some sentencesdo not have an object, then the object within the obtained triple is“null.” For example, some sentences include parallel predicates, thenthe predicate in the obtained triple may include two words. It can beappreciated that, the subject, the predicate, and the object in thetriple may be identical to the subject, the predicate, and the object inthe target text, or different. For example, when the target text is“after the sharing bicycle industry expanded rapidly in 2016 and thefirst half of 2017, the sharing bicycle industry has gradually showndeclining tendency in the second half of 2017,” the obtained triples mayinclude: the sharing bicycle industry-expand-[null], the sharing bicycleindustry-has shown-declining tendency. In the target text, the subjectis “the sharing bicycle industry,” the predicate is “has shown,” theobject is “declining tendency.” Here, the predicate of “expand” withinthe first triple is different from the predicate of “has shown” in thetarget text. The subject, predicate, and object in the second triple areidentical to the subject, predicate, and object in the target text.

Step 204, determining, based on words contained in a triple among the atleast one triple and a predetermined weight of the syntactic structuretree matched to obtain the triple, a target triple among the at leastone triple.

After obtaining the at least one triple, for each triple among the atleast one triple, the executor may determine, based on words containedin the triple and a predetermined weight of the syntactic structure treematched to obtain the triple, a target triple among the at least onetriple. Here, the weight of the syntactic structure tree may be set bythose skilled in the art based on a specific application scenario. Forexample, those skilled in the art may select one syntactic structuretree at a time from the at least one syntactic structure tree to performa matching of triple, and may set the weight based on the number ofsyntactic structure tree being selected and used in the matching oftriple in a past time interval. Alternatively, those skilled in the artmay also set the weight based on the number of nodes contained in thesyntactic structure tree.

With continued reference to FIG. 3, FIG. 3 is a schematic diagram of anapplication scenario of the method for generating information accordingto the present embodiment. In the application scenario of FIG. 3, a userinputs a target text as a video title through a terminal, and theterminal transmits the video title to a server. After receiving thevideo title, the sever may initially generate a dependency tree of thevideo title, and then may match the video title with the syntacticstructure tree to obtain at least one triple, and may further determinea target triple among the at least one triple. Finally, the server mayoutput the target triple to the terminal for the user to check.

The method for generating information provided by the above embodimentof the present disclosure, after receiving a target text, may execute adependency syntax parsing on the target text to generate a dependencytree of the target text. The method then matches a predeterminedsyntactic structure tree with the dependency tree to obtain at least onetriple. Finally, the method determines a target triple among the atleast one triple based on words contained in a triple among the at leastone triple and a predetermined weight of the syntactic structure treematched to obtain the target triple. The method of the presentembodiment may select a triple that is most relevant to an eventcontained in a target text, thereby improving the accuracy of extractinga target triple.

In some alternative implementations of the present embodiment, theexecutor may determine a weight of syntactic structure tree according tofollowing steps, not shown in FIG. 2: first, obtaining at least onehistorical target triple; then, statisticising the number of thehistorical target triples obtained by matchings of a given syntacticstructure tree in the at least one historical target triple; finally,determining a weight of the at least one syntactic structure tree basedon a result of the statisticising.

In the present implementation, the executor may first obtain at leastone historical target triple, a historical target triple is a targettriple obtained through a processing performed by the executor on thereceived target text in a past time interval. Then, the executor maystatisticise the number of the historical target triples obtained bymatching a given syntactic structure tree in the at least one historicaltarget triple. It should be appreciated that the greater is the numberof the historical target triples obtained by matching a certainsyntactic structure tree, the higher the accuracy of the certainsyntactic structure tree is, and the greater the weight of the syntacticstructure tree should be. Finally, the executor may determine theweights of the respective syntactic structure trees based on the resultof the statisticising. For example, the executor obtains one hundred ofhistorical target triples, and after statisticising, finds that fifty ofthe historical target triples are obtained from syntactic structure treea, and thirty of the historical target triples are obtained fromsyntactic structure tree b, and the remaining twenty of historicaltarget triples are obtained from syntactic structure tree c. Theexecutor may determine, based on the result of the statisticising, theweight of syntactic structure tree a as 50/100=0.5, the weight ofsyntactic structure tree b as 30/100=0.3, and the weight of syntacticstructure tree c as 20/100=0.2.

The method for generating information in the present implementation mayadjust in time, in combination with the historical target triple, theweight of syntactic structure tree, thereby improving the accuracy ofdetermining a target triple.

With continued reference to FIG. 4, a flow 400 of determining a targettriple in the method for generating information according to the presentdisclosure is illustrated. As shown in FIG. 4, the present disclosuremay determine the triple according to steps as follow:

Step 401, determining a quantifier and an attributive within a targettext based on the dependency tree.

In the present embodiment, since the category and function of a wordhave been characterized in the dependency tree, the executor maydetermine a quantifier and an attributive within a target text based onthe generated dependency tree of the target text. An attributive is usedto modify a subject or an object, and may include the noun, the pronounand the adjective.

Step 402, determining an objective modified by the quantifier and anobjective modified by the attributive.

After determining the quantifier and the attributive, the executor maydetermine an objective modified by the quantifier and an objectivemodified by the attributive.

The objectives maybe the subject within the triple, or may also be theobject within the triple. For example, in a text of “one apple,” “one”is a quantifier, and “apple” is the objective modified by the quantifier“one”. In a text of “red apple,” the “red” is an attributive, and the“apple” is the objective modified by the attributive “red”.

Step 403, updating the at least one triple based on the determinedquantifier, attributive and objectives.

After determining the quantifier, the attributive, and the objectivesmodified by them, the executor may update at least one triple. Forexample, in response to a determined objective being an object of atriple, the executor may merge the quantifier and/or the attributive,modifying the objective with the objective, and take the merged text asa new object of the triple, thereby updating the triple. Through theupdate, in one respect, words of each triple can be increased, and atarget triple can be determined by number of characters contained in theupdated triple, thereby improving the accuracy of determining a targettriple. For example, when the target text is “Zhang San attends theShenzhen birthday party,” the executor may match, after generating adependency tree, the dependency tree with a syntactic structure tree toobtain a triple of “Zhang San-attends-the Shenzhen,” and a triple of“Zhang San-attends-birthday party”. Based on the dependency tree, “theShenzhen” is determined as an attributive of “birthday party,”accordingly, the executor may update to obtain a triple of “ZhangSan-attends-the Shenzhen birthday party.”

In some alternative implementations of the present embodiment, the step403 may further include content, not shown in FIG. 4, as follows:deleting from the at least one triple the triple of which the objectmatches the attributive within the target text.

In the present implementation, the executor may determine whether thereis a triple of which the object is the attributive of the target textamong the obtained at least one triple. If there is, the executor maydelete the triple. For example, for the triple of “Zhang San-attends-theShenzhen,” the executor may determine that “the Shenzhen,” serving as anattributive, should not serve as an object of the triple. Therefore, theexecutor may determine that the triple of “Zhang San-attends-theShenzhen” is incorrect, and may delete the triple. Thus, the amount ofcalculation can be decreased effectively, and the efficiency ofcalculation can be improved.

In some alternative implementations of the present embodiment, theexecutor may update the triple through steps, that are not shown in FIG.4, as follow: first of all, for a triple among the at least one triple,whether the determined objective matches the subject or object of thetriple is determined; then, after determining that the determinedobjective matches the subject of the triple, the quantifier andattributive modifying the determined objective are merged with thesubject of the triple, and the merged text is determined as the subjectof the triple. Then, after determining that the determined objectivematches the object of the triple, the quantifier and attributivemodifying the determined objective are merged with the object of thetriple, and the merged text is determined as the object of the triple.

For each triple among the at least one triple, the executor may firstdetermine whether the determined objective matches the subject or objectof the triple. It is appreciated that, the “match” herein may indicatethat at least one character of the objective is identical to at leastone character of the subject or object within the triple. For example,when the objective is “Mr. Zhang,” and the subject of the triple is “Mr.Zhang San,” it can be determined that the above objective matches thesubject of the triple.

If it is determined that the determined objective matches the subject ofthe triple, the executor may merge the quantifier and attributivemodifying the above objective with the subject of the triple, and take amerged text as the subject of the triple. For example, when theobjective is “Mr. Zhang,” the attributive modifying the objective is“refreshed,” while the subject of the triple is “Mr. Zhang San,” themerged text may be as “refreshed Mr. Zhang San.” And then, the“refreshed Mr. Zhang San” is took as the subject of the triple, therebyimplementing an update on the subject of the triple.

After determining that the determined objective matches the object ofthe triple, the executor may merge the quantifier and attributivemodifying the objective with the object of the triple, and take a mergedtext as the object of the triple. Thereby implementing an update on theobject of the triple.

It is appreciated that, during updating the triple, the executor mayonly update the subject of the triple, may also only update the objectof the triple, or may even update both the subject and object of thetriple. And, when performing the above merging operation, the executormay take any one of the quantifier or the attributive to merge with thesubject of the triple, or with the object of the triple.

Step 404, determining a target triple among the updated at least onetriple.

After updating the triple, the executor may determine a target tripleamong the updated at least one triple. Specifically, the executor maydetermine a target triple according to the following:

At sub-step 4041, for a triple among the at least one triple,determining a predetermined weight of the syntactic structure treematched to obtain the triple; determining the number of characters inthe words included in the triple; determining a co-occurrence degree ofthe words within the triple; and determining a score of the triple basedon the determined weight, number and co-occurrence degree.

For each triple among the at least one triple, the executor may firstdetermine a weight of the syntactic structure tree which was matched toobtain the triple. Then, the executor may determine the number ofcharacters in the words included in the triple based on the wordsincluded in the triple. And, the executor may determine a co-occurrencedegree of the words within the triple. Finally, the executor maycalculate a score of the triple based on the determined weight, thedetermined number of characters and the determined co-occurrence degree.The co-occurrence herein may indicate a word within the triple appearingin a given sentence, a given paragraph or a given article. Theco-occurrence degree may be a product of the following three: theprobability of the appearance of a first word within the triple, theprobability of the appearance of a second word on the basis of theappearance of the first word, and the probability of the appearance of athird word on the basis of the appearances of the first and secondwords.

For example, the triple is “Zhang San-visits-a newborn”, the executormay first determine the probability of the appearance of “Zhang San” ina predetermined information set. The information set may be a set ofwebpage themes, a set of a plurality of articles, etc. Supposing thatthe information set includes ten thousand pieces of information, withinwhich there is a hundred pieces of the information including “ZhangSan”, then the probability of the appearance of “Zhang San” would be 1%.And, the executor may determine the probability of the appearance of“visits” in the information that is in the information set and including“Zhang San.” Supposing that, within the hundred pieces of informationincluding “Zhang San”, there is twenty pieces of information including“visits”, then, on the basis of the appearance of “Zhang San”, theprobability of the appearance of “visits” would be 20%. Then, theexecutor may determine, according to the same method, that on the basisof the appearances of “Zhang San” and “visits,” the probability of “anewborn” appearing subsequent to the “visits” would be 50%. Accordingly,the co-occurrence degree is 1%×20%×50%=0.1%.

After obtaining the weight, the number of characters and theco-occurrence degree, the executor may determine a score of the tripleaccording to the following formula:score=a×weight+b×the number ofcharacters+c×co-occurrence degree. Where, a, b, or c is a predeterminedcoefficient.

At sub-step 4042, determining a triple with the highest score among theat least one triple as a target triple.

After obtaining the scores of the respective triples, the executor maytake the triple with the highest score among the at least one triple asa target triple. It is appreciated that, the higher the score of thetriple is, which means the higher the accuracy of the triple is, and themore able to express the descriptive information of the objective andthe objective contained in the target text.

The method for generating information provided in the embodiment of thepresent disclosure, may determine a triple that is most relevant to thetarget text among a plurality of triples, thereby improving the accuracyof extracting a triple.

With continued reference to FIG. 5, it illustrates a flow 500 of anotherembodiment of the method for generating information according to thepresent disclosure. As shown in FIG. 5, after obtaining the targettriple, the method for generating information of the present embodimentmay further include:

Step 501, determining at least one piece of historical event informationrelevant to the target text in a predetermined historical eventinformation set based on the target triple.

After determining the target triple, the executor may determine at leastone piece of historical event information relevant to the target text ina predetermined historical event information set based on the targettriple. The historical event information may also include an objectiveand descriptive information of the objective. In the present embodiment,when an objective within the historical event information is identicalto the subject of the target triple, or when the historical eventinformation contains the subject, the predicate or the object of thetarget triple, it can be determined that the historical eventinformation is relevant to the target text.

In some alternative implementations of the present embodiment, thehistorical event information may include participant information andtrigger word information. The executor may determine whether thehistorical event information and the target text are relevant accordingto steps, not shown in FIG. 5, as follow: first, determining whetherfollowing conditions are met: a subject or an object of the targettriple matches the participant information of the historical eventinformation within the historical event information set, or a predicateof the target triple matches the trigger word information of thehistorical event information within the historical event informationset; then, determining the historical event information meeting at leastone of the above conditions being relevant to the target text.

In the present implementation, the participant information may beinformation of a relevant person in a historical event. And the triggerword information may be action information of the participantinformation. For example, when the historical event information is“Xiaoming and Xiaohong go to the first cafeteria together to havelunch”, the participant information may include “Xiaoming” and“Xiaohong,” and the trigger word information is “have.” Matching asubject or an object of the target triple with the participantinformation, if the matching is successful, it means the subject orobject of the triple is identical to the participant of the historicalevent. Matching a predicate of the target triple with the trigger wordinformation, and, if the matching is successful, it means the predicateof the triple is identical to the trigger word in the historical event.When at least one of the two conditions is met, the executor maydetermine that the historical event is relevant to the target text.

Step 502, determining a similarity between the target text and at leastone piece of historical event information.

After determining at least one piece of historical event informationbased on the target triple, to further obtain the historical eventinformation that is most relevant to the target text, the executor maydetermine the similarities between the target text and the respectivepieces of historical event information among the at least one piece ofhistorical event information. The executor may determine a similaritybetween the target text and a piece of historical event informationbased on the number of the same characters or words between the targettext and the piece of historical event information. Otherwise, theexecutor may determine the similarity based on the number of itemsmeeting the above conditions within the historical event information.

In some alternative implementations of the present embodiment, thehistorical event information may include a keyword. The keyword may be aname of an event, an occurring time of an event, etc. Here, the name ofthe event may include a subject, a predicate, and an object of anhistorical event. The executor may determine a similarity between thetarget text and the historical event information, according to the stepsthat are not shown in FIG. 5, as follow: first, the executor may segmentthe target text to obtain a first word set. Then, for a piece ofhistorical event information among the at least one piece of historicalevent information, the executor may concatenate the keywords containedin the piece of historical event information, and may segment the textobtained by the concatenating to obtain a second word set. Then, theexecutor may determine the similarity between the target text and thepiece of historical event information based on the first word set andthe second word set.

In the present implementation, the executor may first segment the targettext to obtain a first word set. During segmenting, words may besegmented based on semantic meanings, or be segmented based on thenumbers of characters. Then, For each piece of historical eventinformation among the at least one piece of historical eventinformation, the executor may concatenate the keywords contained in thepiece of historical event information, and may segment the text obtainedby the concatenating to obtain a second word set. To guarantee theaccuracy of the similarity, the text may be segmented at a samegranularity. That is, when segmenting the target text and the textobtained by the concatenating, the text are segmented in bigram ortrigram model, the numbers of characters contained in the obtained wordsare identical. For example, when the target text is“wo-shi-zhong-guo-ren,” and the target text is segmented in bigrammodel, then the words “wo-shi,” “shi-zhong,” “zhong-guo,” and “guo-ren,”are obtained, or the target text is segmented in tigram model, then thewords “wo-shi-zhong,” “shi-zhong-guo,” and “zhong-guo-ren” are obtained.

The executor, after obtaining the first word set and the second wordset, may list up the words within the first word set and the second wordset. Then the executor may statistic the number of appearance of each ofthe above words in the target text, and then combine the respectiveobtained numbers to obtain a first word vector A. The executor maystatistic the number of appearance of each of the above words in thetext obtained by concatenating, and may combine the respective obtainednumbers to form a second word vector B. Then, the executor may calculatethe similarity between the target text and the text obtained byconcatenating according to the vector cosine formula:

${\cos \mspace{11mu} \theta} = {\frac{\sum\limits_{i = 1}^{n}\left( {A_{i} \times B_{i}} \right)}{\sqrt{\sum\limits_{i = 1}^{n}A_{i}^{2}} \times \sqrt{\sum\limits_{i = 1}^{n}B_{i}^{2}}}.}$

Where, A=(A₁, A₂, . . . , A_(n)), B=(B₁, B₂, . . . , B_(n)). Ai is thei-th value of the first word vector A, and B₁ is the i-th value of thesecond word vector B.

Step 503, outputting historical event information having the highestsimilarity to the target text.

After determining the similarity between the target text and each pieceof historical event information relevant to the target text, theexecutor may output the piece of historical event information having thehighest similarity to the target text.

The method for generating information provided in the embodiment of thepresent disclosure may determine historical event information that ismost relevant to the target text from the historical event informationset, to enrich users' information content. The method of the presentembodiment may be utilized in the aspect of video selection. The titleof video may be took as a target text, and a target triple of the titleof video is determined, then, a historical event relevant to the titleof video is selected, thereby whether the video is an old video isdetermined.

Further referring to FIG. 6, as an implementation of the method shown inthe above figures, the disclosure provides an embodiment of an apparatusfor generating information. The embodiment of the apparatus correspondsto the embodiment of the method shown in FIG. 2. The apparatus may bespecifically applied to a variety of electronic devices.

As shown in FIG. 6, the apparatus 600 for generating information, of thepresent embodiment, includes: a target text receiving unit 601, adependency tree generating unit 602, a triple determining unit 603, anda target triple determining unit 604.

The target text receiving unit 601 is configured to receive a targettext, the target text including an objective and descriptive informationof the objective.

The dependency tree generating unit 602 is configured to perform adependency syntax parsing on the target text to generate a dependencytree of the target text.

The triple determining unit 603 is configured to match predetermined atleast one syntactic structure tree with the dependency tree to obtain atleast one triple. Where the triple includes a subject, a predicate, andan object.

The target triple determining unit 604 is configured to determine, basedon words contained in a triple among the at least one triple and apredetermined weight of the syntactic structure tree matched to obtainthe triple, a target triple among the at least one triple.

In some alternative implementations of the present embodiment, thetarget triple determining unit 604 may further include an attributivedetermining module, an objective determining module, a triple updatingmodule and a target triple determining module, which are not shown inFIG. 6.

The attributive determining module is configured to determine aquantifier and an attributive within the target text based on thedependency tree.

The objective determining module is configured to determine an objectivemodified by the quantifier and an objective modified by the attributive.

The triple updating module is configured to update the at least onetriple based on the determined quantifier, attributive and objectives.

The target triple determining module is configured to determine thetarget triple among the updated at least one triple.

In some alternative implementations of the present embodiment, thetriple updating module may be further configured to: for the tripleamong the at least one triple, determine whether the determinedobjective matches the subject or object of the triple; merge, inresponse to determining that the determined objective matches thesubject of the triple, the quantifier and attributive modifying thedetermined objective with the subject of the triple, and determine amerged text as the subject of the triple; and merge, in response todetermining that the determined objective matches the object of thetriple, the quantifier and attributive modifying the determinedobjective with the object of the triple, and determine a merged text asthe object of the triple.

In some alternative implementations of the present embodiment, thetarget triple determining unit may be further configured to: for thetriple among the at least one triple, determine the predetermined weightof the syntactic structure tree matched to obtain the triple; determinea number of characters of the words in the triple, determine aco-occurrence degree of the words within the triple, and determine ascore of the triple according to the determined weight, number andco-occurrence degree; and determine a triple with the highest scoreamong the at least one triple as a target triple.

In some alternative implementations of the present embodiment, theapparatus 600 may further include a weight setting unit that is notshown in FIG. 6. The weight setting unit may include a historical targettriple module, a triple number statisticising module, and a weightdetermining module.

The historical target triple module is configured to obtain at least onehistorical target triple.

The triple number statisticising module is configured to statisticise anumber of the historical target triples obtained by matching a givensyntactic structure tree in the at least one historical target triple.

The weight determining module is configured to determine a weight of theat least one syntactic structure tree based on a result of thestatisticising.

In some alternative implementations of the present embodiment, theapparatus 600 may further include a historical event informationdetermining unit, a similarity determining unit, and a historical eventinformation outputting unit, which are not shown in FIG. 6.

The historical event information determining unit is configured todetermine at least one piece of historical event information relevant tothe target text in a predetermined historical event information setbased on the target triple.

The similarity determining unit is configured to determine a similaritybetween a target text and the at least one piece of historical eventinformation.

The historical event information outputting unit is configured to outputhistorical event information having the highest similarity to the targettext.

In some alternative implementations of the present embodiment, thehistorical event information may include participant information andtrigger word information. The historical event information determiningunit is further configured to: determine whether following conditionsare met: a subject or an object of the target triple matches theparticipant information of the historical event information within thehistorical event information set, or a predicate of the target triplematches the trigger word information of the historical event informationwithin the historical event information set; and determine thehistorical event information meeting at least one of the aboveconditions being relevant to the target text.

In some alternative implementations of the present embodiment, thehistorical event information may include keywords. The similaritydetermining unit is further configured to: segment the target text toobtain a first word set; and for the historical event information amongthe at least one piece of historical event information, concatenatekeywords in the historical event information, segment a text obtained byconcatenating, to obtain a second word set; and determine a similaritybetween the target text and the historical event information based onthe first word set and the second word set.

The apparatus for generating information provided in the embodiment ofthe present disclosure, after receiving a target text, may perform adependency syntax parsing on the target text to generate a dependencytree of the target text; and then match the predetermined at least onesyntactic structure tree with the dependency tree to obtain at least onetriple; finally, determine a target triple among the at least one triplebased on the words contained in a triple among the at least one triple,and a predetermined weight of the syntactic structure tree matched toobtain the triple. The apparatus of the present embodiment can pick outa triple that is most relevant to the event contained in the targettext, thereby improving the accuracy of extracting a target triple.

It should be understood that the unit 601 to unit 604, which are recitedin the apparatus 600 for generating information, correspond to steps ofthe method described in FIG. 2. Thus, the operations and features,described above for the method for generating information, are equallyapplicable to the apparatus 600 and the units included therein, anddetailed description thereof will be omitted.

Referring to FIG. 7, a schematic structural diagram of a computer system700 adapted to implement the apparatus of the embodiments of the presentdisclosure is shown. The apparatus shown in FIG. 7 is merely an exampleand should not impose any restriction on the function and scope of useof the embodiments of the present disclosure.

As shown in FIG. 7, the computer system 700 includes a centralprocessing unit (CPU) 701, which may execute various appropriate actionsand processes in accordance with a program stored in a read-only memory(ROM) 702 or a program loaded into a random access memory (RAM) 703 froma storage portion 708. The RAM 703 also stores various programs and datarequired by operations of the system 700. The CPU 701, the ROM 702 andthe RAM 703 are connected to each other through a bus 704. Aninput/output (I/O) interface 705 is also connected to the bus 704.

The following components are connected to the I/O interface 705: aninput portion 706 including a keyboard, a mouse etc.; an output portion707 comprising a cathode ray tube (CRT), a liquid crystal display device(LCD), a speaker etc.; a storage portion 708 including a hard disk andthe like; and a communication portion 709 comprising a network interfacecard, such as a LAN card and a modem. The communication portion 709performs communication processes via a network, such as the Internet. Adrive 710 is also connected to the I/O interface 705 as required. Aremovable medium 711, such as a magnetic disk, an optical disk, amagneto-optical disk, and a semiconductor memory, may be installed onthe drive 710, to facilitate the retrieval of a computer program fromthe removable medium 711, and the installation thereof on the storageportion 708 as needed.

In particular, according to embodiments of the present disclosure, theprocess described above with reference to the flow chart may beimplemented in a computer software program. For example, an embodimentof the present disclosure includes a computer program product, whichcomprises a computer program that is tangibly embedded in amachine-readable medium. The computer program comprises program codesfor executing the method as illustrated in the flow chart. In such anembodiment, the computer program may be downloaded and installed from anetwork via the communication portion 709, and/or may be installed fromthe removable media 711. The computer program, when executed by thecentral processing unit (CPU) 701, implements the above mentionedfunctionalities as defined by the methods of the present disclosure.

It should be noted that the computer readable medium in the presentdisclosure maybe computer readable signal medium or computer readablestorage medium or any combination of the above two. An example of thecomputer readable storage medium may include, but not limited to:electric, magnetic, optical, electromagnetic, infrared, or semiconductorsystems, apparatus, elements, or a combination any of the above. A morespecific example of the computer readable storage medium may include butis not limited to: electrical connection with one or more wire, aportable computer disk, a hard disk, a random access memory (RAM), aread only memory (ROM), an erasable programmable read only memory (EPROMor flash memory), a fibre, a portable compact disk read only memory(CD-ROM), an optical memory, a magnet memory or any suitable combinationof the above.

In the present disclosure, the computer readable storage medium may beany physical medium containing or storing programs which can be used bya command execution system, apparatus or element or incorporatedthereto. In the present disclosure, the computer readable signal mediummay include data signal in the base band or propagating as parts of acarrier wave, in which computer readable program codes are carried. Thepropagating signal may take various forms, including but not limited to:an electromagnetic signal, an optical signal or any suitable combinationof the above. The computer readable medium may be any computer readablemedium except for the computer readable storage medium. The computerreadable medium is capable of transmitting, propagating or transferringprograms for use by, or used in combination with, a command executionsystem, apparatus or element. The program codes contained on thecomputer readable medium may be transmitted with any suitable mediumincluding but not limited to: wireless, wired, optical cable, RF mediumetc., or any suitable combination of the above.

A computer program code for executing operations in the disclosure maybe compiled using one or more programming languages or combinationsthereof. The programming languages include objective-orientedprogramming languages, such as Java, Smalltalk or C++, and also includeconventional procedural programming languages, such as “C” language orsimilar programming languages. The program code may be completelyexecuted on a user's computer, partially executed on a user's computer,executed as a separate software package, partially executed on a user'scomputer and partially executed on a remote computer, or completelyexecuted on a remote computer or server. In the circumstance involving aremote computer, the remote computer may be connected to a user'scomputer through any network, including local area network (LAN) or widearea network (WAN), or may be connected to an external computer (forexample, connected through Internet using an Internet service provider).

The flow charts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations that may beimplemented according to the systems, methods and computer programproducts of the various embodiments of the present disclosure. In thisregard, each of the blocks in the flow charts or block diagrams mayrepresent a module, a program segment, or a code portion, said module,program segment, or code portion comprising one or more executableinstructions for implementing specified logic functions. It should alsobe noted that, in some alternative implementations, the functionsdenoted by the blocks may occur in a sequence different from thesequences shown in the figures. For example, any two blocks presented insuccession may be executed, substantially in parallel, or they maysometimes be in a reverse sequence, depending on the function involved.It should also be noted that each block in the block diagrams and/orflow charts as well as a combination of blocks may be implemented usinga dedicated hardware-based system executing specified functions oroperations, or by a combination of a dedicated hardware and computerinstructions.

The units involved in the embodiments of the present disclosure maybeimplemented by means of software or hardware. The described units mayalso be provided in a processor, for example, described as: a processor,comprising a target text receiving unit, a dependency tree generatingunit, a triple determining unit, and a target triple determining unit.Where the names of these units do not in some cases constitute alimitation to such units themselves. For example, the target textreceiving unit may also be described as “a unit for receiving a targettext.”

In another aspect, the present disclosure further provides acomputer-readable medium. The computer-readable medium may be thecomputer-readable medium included in the apparatus in the abovedescribed embodiments, or a stand-alone computer-readable medium notassembled into the apparatus. The computer-readable medium stores one ormore programs. The one or more programs, when executed by a device,cause the device to: receive a target text, the target text comprisingan objective and descriptive information of the objective; perform adependency syntax parsing on the target text to generate a dependencytree of the target text; match the predetermined at least one syntacticstructure tree with the dependency tree to obtain at least one triple, atriple comprising a subject, a predicate, and an object; and determine,based on words contained in a triple among the at least one triple and apredetermined weight of the syntactic structure tree matched to obtainthe triple, a target triple among the at least one triple.

The above description only provides an explanation of the preferredembodiments of the present disclosure and the technical principles used.It should be appreciated by those skilled in the art that the inventivescope of the present disclosure is not limited to the technicalsolutions formed by the particular combinations of the above-describedtechnical features. The inventive scope should also cover othertechnical solutions formed by any combinations of the above-describedtechnical features or equivalent features thereof without departing fromthe concept of the disclosure. Technical schemes formed by theabove-described features being interchanged with, but not limited to,technical features with similar functions disclosed in the presentdisclosure are examples.

What is claimed is:
 1. A method for generating information, the methodcomprising: receiving a target text, the target text comprising anobjective and descriptive information of the objective; performing adependency syntax parsing on the target text to generate a dependencytree of the target text; matching predetermined at least one syntacticstructure tree with the dependency tree to obtain at least one triple, atriple comprising a subject, a predicate, and an object; anddetermining, based on words contained in a triple among the at least onetriple and a predetermined weight of the syntactic structure treematched to obtain the triple, a target triple among the at least onetriple.
 2. The method according to claim 1, wherein the determining,based on words contained in a triple among the at least one triple and apredetermined weight of the syntactic structure tree matched to obtainthe triple, a target triple among the at least one triple, comprises:determining a quantifier and an attributive within the target text basedon the dependency tree; determining an objective modified by thequantifier and an objective modified by the attributive; updating the atleast one triple based on the determined quantifier, attributive andobjectives; and determining the target triple among updated at least onetriple.
 3. The method according to claim 2, wherein the updating the atleast one triple based on the determined quantifier, attributive andobjectives, comprises: for the triple among the at least one triple,determining whether the determined objective matches the subject orobject of the triple; merging, in response to determining that thedetermined objective matches the subject of the triple, the quantifierand attributive modifying the determined objective with the subject ofthe triple, and determining a merged text as the subject of the triple;and merging, in response to determining that the determined objectivematches the object of the triple, the quantifier and attributivemodifying the determined objective with the object of the triple, anddetermining a merged text as the object of the triple.
 4. The methodaccording to claim 1, wherein the determining, based on words containedin a triple among the at least one triple and a predetermined weight ofthe syntactic structure tree matched to obtain the triple, a targettriple among the at least one triple, comprises: for the triple amongthe at least one triple, determining the predetermined weight of thesyntactic structure tree matched to obtain the triple; determining anumber of characters of the words in the triple, determining aco-occurrence degree of the words within the triple, and determining ascore of the triple according to the determined weight, number andco-occurrence degree; and determining a triple with a highest scoreamong the at least one triple as the target triple.
 5. The methodaccording to claim 2, wherein the determining, based on words containedin a triple among the at least one triple and a predetermined weight ofthe syntactic structure tree matched to obtain the triple, a targettriple among the at least one triple, comprises: for the triple amongthe at least one triple, determining the predetermined weight of thesyntactic structure tree matched to obtain the triple; determining anumber of characters of the words in the triple, determining aco-occurrence degree of the words within the triple, and determining ascore of the triple according to the determined weight, number andco-occurrence degree; and determining a triple with a highest scoreamong the at least one triple as the target triple.
 6. The methodaccording to claim 3, wherein the determining, based on words containedin a triple among the at least one triple and a predetermined weight ofthe syntactic structure tree matched to obtain the triple, a targettriple among the at least one triple, comprises: for the triple amongthe at least one triple, determining the predetermined weight of thesyntactic structure tree matched to obtain the triple; determining anumber of characters of the words in the triple, determining aco-occurrence degree of the words within the triple, and determining ascore of the triple according to the determined weight, number andco-occurrence degree; and determining a triple with a highest scoreamong the at least one triple as the target triple.
 7. The methodaccording to claim 1, further comprising: obtaining at least onehistorical target triple; statisticising a number of historical targettriples obtained by matching a given syntactic structure tree in the atleast one historical target triple; and determining a weight of the atleast one syntactic structure tree based on a result of thestatisticising.
 8. The method according to claim 2, further comprising:obtaining at least one historical target triple; statisticising a numberof historical target triples obtained by matching a given syntacticstructure tree in the at least one historical target triple; anddetermining a weight of the at least one syntactic structure tree basedon a result of the statisticising.
 9. The method according to claim 1,the method further comprising: determining at least one piece ofhistorical event information relevant to the target text in apredetermined historical event information set based on the targettriple; determining a similarity between the target text and the atleast one piece of historical event information; and outputtinghistorical event information having highest similarity to the targettext.
 10. The method according to claim 9, wherein the historical eventinformation comprises participant information and trigger wordinformation; and the determining at least one piece of historical eventinformation relevant to the target text in a predetermined historicalevent information set based on the target triple comprises: determiningwhether following conditions are met: a subject or an object of thetarget triple matches the participant information of the historicalevent information within the historical event information set, or apredicate of the target triple matches the trigger word information ofthe historical event information within the historical event informationset; and determining the historical event information meeting at leastone of the above conditions being relevant to the target text.
 11. Themethod according to claim 9, wherein the historical event informationcomprises keywords; and the determining a similarity between the targettext and the at least one piece of historical event informationcomprises: segmenting the target text to obtain a first word set; andfor the historical event information among the at least one piece ofhistorical event information, concatenating keywords in the historicalevent information, segmenting a text obtained by concatenating, toobtain a second word set; and determining a similarity between thetarget text and the historical event information based on the first wordset and the second word set.
 12. An apparatus for generatinginformation, comprising: at least one processor; and a memory storinginstructions, the instructions when executed by the at least oneprocessor, cause the at least one processor to perform operations, theoperations comprising: receiving a target text, the target textcomprising an objective and descriptive information of the objective;performing a dependency syntax parsing on the target text to generate adependency tree of the target text; matching predetermined at least onesyntactic structure tree with the dependency tree to obtain at least onetriple, a triple comprising a subject, a predicate, and an object; anddetermining, based on words contained in a triple among the at least onetriple and a predetermined weight of the syntactic structure treematched to obtain the triple, a target triple among the at least onetriple.
 13. The apparatus according to claim 12, wherein thedetermining, based on words contained in a triple among the at least onetriple and a predetermined weight of the syntactic structure treematched to obtain the triple, a target triple among the at least onetriple, comprises: determining a quantifier and an attributive withinthe target text based on the dependency tree; determining an objectivemodified by the quantifier and an objective modified by the attributive;updating the at least one triple based on the determined quantifier,attributive and objectives; and determining the target triple amongupdated at least one triple.
 14. The apparatus according to claim 13,wherein the updating the at least one triple based on the determinedquantifier, attributive and objects, comprises: for the triple among theat least one triple, determining whether the determined objectivematches the subject or object of the triple; merging, in response todetermining that the determined objective matches the subject of thetriple, the quantifier and attributive modifying the determinedobjective with the subject of the triple, and determining a merged textas the subject of the triple; and merging, in response to determiningthat the determined objective matches the object of the triple, thequantifier and attributive modifying the determined objective with theobject of the triple, and determining a merged text as the object of thetriple.
 15. The apparatus according to claim 12, wherein thedetermining, based on words contained in a triple among the at least onetriple and a predetermined weight of the syntactic structure treematched to obtain the triple, a target triple among the at least onetriple, comprises: for the triple among the at least one triple,determining the predetermined weight of the syntactic structure treematched to obtain the triple; determining a number of characters of thewords in the triple, determining a co-occurrence degree of the wordswithin the triple, and determining a score of the triple according tothe determined weight, number and co-occurrence degree; and determininga triple with a highest score among the at least one triple as thetarget triple.
 16. The apparatus according to claim 12, wherein theoperations further comprise: obtaining at least one historical targettriple; statisticising a number of historical target triples obtained bymatching a given syntactic structure tree in the at least one historicaltarget triple; and determining a weight of the at least one syntacticstructure tree based on a result of the statisticising.
 17. Theapparatus according to claim 12, the operations further comprising:determining at least one piece of historical event information relevantto the target text in a predetermined historical event information setbased on the target triple; determining a similarity between the targettext and the at least one piece of historical event information; andoutputting historical event information having highest similarity to thetarget text.
 18. The apparatus according to claim 17, wherein thehistorical event information comprises participant information andtrigger word information; and the determining at least one piece ofhistorical event information relevant to the target text in apredetermined historical event information set based on the targettriple comprises: determining whether following conditions are met: asubject or an object of the target triple matches the participantinformation of the historical event information within the historicalevent information set, or a predicate of the target triple matches thetrigger word information of the historical event information within thehistorical event information set; and determining the historical eventinformation meeting at least one of the above conditions being relevantto the target text.
 19. The apparatus according to claim 17, wherein thehistorical event information comprises keywords; and the determining asimilarity between the target text and the at least one piece ofhistorical event information comprises: segmenting the target text toobtain a first word set; and for the historical event information amongthe at least one piece of historical event information, concatenatingkeywords in the historical event information, segmenting a text obtainedby concatenating, to obtain a second word set; and determining asimilarity between the target text and the historical event informationbased on the first word set and the second word set.
 20. Anon-transitory computer-readable medium, storing a computer program,wherein the computer program, when executed by a processor, cause theprocessor to perform operations, the operations comprising: receiving atarget text, the target text comprising an object and descriptiveinformation of the object; performing a dependency syntax parsing on thetarget text to generate a dependency tree of the target text; matchingpredetermined at least one syntactic structure tree with the dependencytree to obtain at least one triple, a triple comprising a subject, apredicate, and an object; and determining, based on words contained in atriple among the at least one triple and a predetermined weight of thesyntactic structure tree matched to obtain the triple, a target tripleamong the at least one triple.